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--- |
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license: mit |
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language: |
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- en |
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--- |
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## Model Details |
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**Model Name:** `Canstralian/pentest_ai` |
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**Base Model:** `WhiteRabbitNeo/WhiteRabbitNeo-13B-v1` |
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**Model Version:** `1.0.0` |
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## Intended Use |
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The **Canstralian/pentest_ai** model is specifically designed for **penetration testing** applications. It assists security professionals and ethical hackers in automating and enhancing security assessment tasks. The model is well-suited for generating reconnaissance strategies, conducting vulnerability assessments, report generation, and automating scripting tasks related to penetration testing. |
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## How to Use |
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To utilize the **Canstralian/pentest_ai** model, ensure you have the `transformers` library installed, and load the model as follows: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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# Load the tokenizer and model |
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tokenizer = AutoTokenizer.from_pretrained("Canstralian/pentest_ai") |
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model = AutoModelForCausalLM.from_pretrained("Canstralian/pentest_ai") |
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# Example usage |
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input_text = "Generate a reconnaissance plan for the target network." |
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inputs = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(**inputs) |
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generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(generated_text) |
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